from sklearn_benchmarks.report import Reporting
reporting = Reporting(config_file_path="config.yml")
reporting.run()
| hour | min | sec | |
|---|---|---|---|
| algo | |||
| KNeighborsClassifier | 0.0 | 13.0 | 39.490230 |
| daal4py_KNeighborsClassifier | 0.0 | 5.0 | 15.148581 |
| KNeighborsClassifier_kd_tree | 0.0 | 6.0 | 44.865003 |
| daal4py_KNeighborsClassifier_kd_tree | 0.0 | 1.0 | 45.613518 |
| KMeans | 0.0 | 12.0 | 32.549946 |
| daal4py_KMeans | 0.0 | 8.0 | 24.129848 |
| LogisticRegression | 0.0 | 0.0 | 4.227819 |
| daal4py_LogisticRegression | 0.0 | 0.0 | 4.533975 |
| Ridge | 0.0 | 0.0 | 0.999709 |
| daal4py_Ridge | 0.0 | 0.0 | 0.716903 |
| total | 0.0 | 48.0 | 32.353383 |
import pandas as pd
df_skl = pd.read_csv("results/benchmarking/sklearn_LogisticRegression.csv")
display(df_skl)
df_d4p = pd.read_csv("results/benchmarking/daal4py_LogisticRegression.csv")
display(df_d4p)
| estimator | function | mean | stdev | n_samples_train | n_samples | n_features | hyperparams_digest | dataset_digest | C | ... | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | n_iter | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 0.209276 | 0.005438 | 1000 | 1000 | 1000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 87df8708b64e2831c82c5e60d77c73aa | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | [46] |
| 1 | LogisticRegression | predict | 0.000093 | 0.000052 | 1000 | 1 | 1000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 87df8708b64e2831c82c5e60d77c73aa | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | NaN |
| 2 | LogisticRegression | predict | 0.000336 | 0.000079 | 1000 | 100 | 1000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 87df8708b64e2831c82c5e60d77c73aa | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | NaN |
| 3 | LogisticRegression | fit | 0.127952 | 0.001868 | 10000 | 10000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 42d2d17798f0471ec3d5da7cbdf7681d | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | [20] |
| 4 | LogisticRegression | predict | 0.000087 | 0.000052 | 10000 | 1 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 42d2d17798f0471ec3d5da7cbdf7681d | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | NaN |
| 5 | LogisticRegression | predict | 0.000330 | 0.000071 | 10000 | 1000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 42d2d17798f0471ec3d5da7cbdf7681d | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | NaN |
6 rows × 25 columns
| estimator | function | mean | stdev | n_samples_train | n_samples | n_features | hyperparams_digest | dataset_digest | C | ... | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | n_iter | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 0.234960 | 0.022490 | 1000 | 1000 | 1000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 87df8708b64e2831c82c5e60d77c73aa | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | [51] |
| 1 | LogisticRegression | predict | 0.000351 | 0.000113 | 1000 | 1 | 1000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 87df8708b64e2831c82c5e60d77c73aa | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | NaN |
| 2 | LogisticRegression | predict | 0.000508 | 0.000111 | 1000 | 100 | 1000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 87df8708b64e2831c82c5e60d77c73aa | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | NaN |
| 3 | LogisticRegression | fit | 0.137598 | 0.001401 | 10000 | 10000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 42d2d17798f0471ec3d5da7cbdf7681d | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | [21] |
| 4 | LogisticRegression | predict | 0.000276 | 0.000105 | 10000 | 1 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 42d2d17798f0471ec3d5da7cbdf7681d | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | NaN |
| 5 | LogisticRegression | predict | 0.000379 | 0.000103 | 10000 | 1000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 42d2d17798f0471ec3d5da7cbdf7681d | 1.0 | ... | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | NaN |
6 rows × 25 columns